STA 141A Final Project: Predictive Model of Mice Neural Activity
Nathaniel Faxon 917100572
2023-04-18
Abstract
Put the abstract here
1st Task: Understanding Data
- Abstract -> What you did in the project (3-4 sentences)
Introduction
- Introduction
Questions of interest along with real-world motivation
- In the original study, the neural activity of 30,000 neurons in 42 brain regions of mice were analyzed. There were 10 mice analyzed over 39 different sessions. Within each session, visual stimuli were presented to the mice at random, then the reaction of the mice to this stimuli were recorded.
- In this project, we will be analyzing the different trials and recorded neuron spikes in order to make a predictive model.
Impact of results
Basic information of data (source, key variables, hypotheses), method, and why.
The data comes from 18 different session of 4 different mice brain neural activity. The four mice that we are studying the neural responses of are Cori, Forssmann, Hench, and Lederberg. More specifically, the first three sessions analyzed Cori, the next four Forssmann, the next four Hench, and the last seven Lederberg. These sessions were taken between 12/14/2016 through 12/11/2017.
Within each session, there were many trials where the activity of the neurons in the mice’s visual cortex was recorded based on their decision making when faced with visual stimuli. Their decision making consists of turning the wheel either to the left or right or holding it still. Different variables were recorded for the 4 different mice. 8 variables are recorded each session:
- brain_area: The area of the brain where each neuron lives.
- contrast_left: Contrast on the left stimulus. Can take values 0, 0.25, 0.5, 1 (0 indicating the absence of a stimulus).
- contrast_right: Contrast on the right stimulus. Can take values 0, 0.25, 0.5, 1 (0 indicating the absence of a stimulus).
- date_exp: Date of the experiment on the mouse.
- feedback_type: The type of feedback that was administered base on
the outcome of the mouse’s decision. There are four different cases that
can happen:
- When the right contrast is greater than the left contrast: If the mouse turned the wheel to the left, feedback_type = 1. If turned to the right, feedback_type = -1.
- When the left contrast is greater than the right contrast: If the mouse turned the wheel to the right, feedback_type = 1. If turned to the left, feedback_type = -1.
- When the left contrast and right contrast equal 0: If mouse held the wheel still, feedback_type = 1. Otherwise, feedback_type = -1.
- When the left contrast and right contrast are equal and not both 0: There is a 50% chance that feedback_type = 1 or -1.
- mouse_name: Name of the mouse being used in the experiment (trial). Specific to this project, the 4 mice are Cori, Forssmann, Hench, and Lederberg.
- spks: Numbers of spikes of neurons in the visual cortex. Holds many different trials.
- time: Center of the time bins for the spks.
Existing research and known results
Exploratory Analysis
- Exploratory Analysis
- Basic descriptive statistics (missing, mean, quartiles, # of observations)
- Data visualization (features, patterns in data)
Methodology
- Methodology:
- Data Integration: PCA, clustering (improving prediction performance)
- Predictive Model (GLM, linear regression, time series)
Data Integration
Predictive Modeling
Result (prediction performance)
Conclusion, Discussion
- Findings, variable information, drawbacks/disadvantages, next steps
- References
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## smithssuppressWarnings(library(ggcorrplot))session=list()
for(i in 1:18){
session[[i]]=readRDS(paste('./Data/session',i,'.rds',sep=''))
#print(session[[i]]$mouse_name)
#print(session[[i]]$date_exp)
}Understanding the DataSet
Information of each session
# Get the variables recorded within this session
print(ls(session[[1]])) # or names(session[[1]]))## [1] "brain_area" "contrast_left" "contrast_right" "date_exp"
## [5] "feedback_type" "mouse_name" "spks" "time"#Determine the different variables and details within each session
uniqueBrainareasVector <- c()
for (i in 1:18) {
message("Session ", i)
session_i <- session[[i]]
print(session[[i]]$mouse_name)
print(session[[i]]$date_exp)
# Get the affected brain areas within this session
print("Affected brain areas:")
print(unique(session_i$brain_area))
uniqueBrainareasVector <- append(uniqueBrainareasVector, unique(session_i$brain_area))
# Get the number of trials within the session
print("Number of trials:")
print(length(session_i$feedback_type))
}## Session 1## [1] "Cori"
## [1] "2016-12-14"
## [1] "Affected brain areas:"
## [1] "ACA" "MOs" "LS" "root" "VISp" "CA3" "SUB" "DG"
## [1] "Number of trials:"
## [1] 114## Session 2## [1] "Cori"
## [1] "2016-12-17"
## [1] "Affected brain areas:"
## [1] "CA1" "VISl" "root" "VISpm" "POST"
## [1] "Number of trials:"
## [1] 251## Session 3## [1] "Cori"
## [1] "2016-12-18"
## [1] "Affected brain areas:"
## [1] "DG" "VISam" "MG" "CA1" "SPF" "root" "LP" "MRN" "POST"
## [10] "NB" "VISp"
## [1] "Number of trials:"
## [1] 228## Session 4## [1] "Forssmann"
## [1] "2017-11-01"
## [1] "Affected brain areas:"
## [1] "LGd" "DG" "TH" "SUB" "VPL" "VISp" "CA1" "VISa" "LSr" "ACA"
## [11] "MOs"
## [1] "Number of trials:"
## [1] 249## Session 5## [1] "Forssmann"
## [1] "2017-11-02"
## [1] "Affected brain areas:"
## [1] "VISa" "root" "CA1" "SUB" "DG" "OLF" "ORB" "ACA" "PL" "MOs"
## [1] "Number of trials:"
## [1] 254## Session 6## [1] "Forssmann"
## [1] "2017-11-04"
## [1] "Affected brain areas:"
## [1] "AUD" "root" "SSp" "CA1" "TH"
## [1] "Number of trials:"
## [1] 290## Session 7## [1] "Forssmann"
## [1] "2017-11-05"
## [1] "Affected brain areas:"
## [1] "VPL" "root" "CA3" "LD" "CP" "EPd" "SSp" "PIR"
## [1] "Number of trials:"
## [1] 252## Session 8## [1] "Hench"
## [1] "2017-06-15"
## [1] "Affected brain areas:"
## [1] "ILA" "TT" "MOs" "PL" "LSr" "root" "LD" "PO" "CA3" "VISa"
## [11] "CA1" "LP" "DG" "VISp" "SUB"
## [1] "Number of trials:"
## [1] 250## Session 9## [1] "Hench"
## [1] "2017-06-16"
## [1] "Affected brain areas:"
## [1] "TT" "ORBm" "PL" "LSr" "root" "CA3" "VISl" "CA1" "TH"
## [10] "VISam" "VPL" "LD"
## [1] "Number of trials:"
## [1] 372## Session 10## [1] "Hench"
## [1] "2017-06-17"
## [1] "Affected brain areas:"
## [1] "MB" "VISp" "SCm" "SCsg" "POST" "DG" "MRN" "CA1" "VISl"
## [10] "POL" "root" "GPe" "VISrl"
## [1] "Number of trials:"
## [1] 447## Session 11## [1] "Hench"
## [1] "2017-06-18"
## [1] "Affected brain areas:"
## [1] "MOp" "LSc" "root" "PT" "CP" "LSr"
## [1] "Number of trials:"
## [1] 342## Session 12## [1] "Lederberg"
## [1] "2017-12-05"
## [1] "Affected brain areas:"
## [1] "VISp" "DG" "SUB" "LGd" "PL" "root" "MOs" "ACA" "CA1"
## [10] "VISam" "MD" "LH"
## [1] "Number of trials:"
## [1] 340## Session 13## [1] "Lederberg"
## [1] "2017-12-06"
## [1] "Affected brain areas:"
## [1] "VISam" "ZI" "DG" "CA1" "LGd" "MB" "SCs" "RN" "MRN"
## [10] "SCm" "ACA" "PL" "MS" "root" "MOs"
## [1] "Number of trials:"
## [1] 300## Session 14## [1] "Lederberg"
## [1] "2017-12-07"
## [1] "Affected brain areas:"
## [1] "ORB" "MOs" "root" "MRN" "SCm" "SCs" "VISp" "RSP" "CA1" "PAG"
## [1] "Number of trials:"
## [1] 268## Session 15## [1] "Lederberg"
## [1] "2017-12-08"
## [1] "Affected brain areas:"
## [1] "BLA" "GPe" "root" "VPM" "LGd" "ZI" "MB" "CA3"
## [1] "Number of trials:"
## [1] 404## Session 16## [1] "Lederberg"
## [1] "2017-12-09"
## [1] "Affected brain areas:"
## [1] "SSs" "SSp" "MB" "TH" "LGd" "CA3"
## [1] "Number of trials:"
## [1] 280## Session 17## [1] "Lederberg"
## [1] "2017-12-10"
## [1] "Affected brain areas:"
## [1] "root" "VPL" "VPM" "RT" "MEA" "LD"
## [1] "Number of trials:"
## [1] 224## Session 18## [1] "Lederberg"
## [1] "2017-12-11"
## [1] "Affected brain areas:"
## [1] "CP" "ACB" "OT" "SI" "SNr" "LGd" "ZI" "CA3" "root" "TH"
## [1] "Number of trials:"
## [1] 216# Get the unique brainareas through all sessions
uniqueBrainareasVector <- unique(uniqueBrainareasVector)
uniqueBrainareasVectorLength = length(uniqueBrainareasVector)
message("There are ", uniqueBrainareasVectorLength, " unique brain areas throughout all sessions:")## There are 62 unique brain areas throughout all sessions:print(uniqueBrainareasVector)## [1] "ACA" "MOs" "LS" "root" "VISp" "CA3" "SUB" "DG" "CA1"
## [10] "VISl" "VISpm" "POST" "VISam" "MG" "SPF" "LP" "MRN" "NB"
## [19] "LGd" "TH" "VPL" "VISa" "LSr" "OLF" "ORB" "PL" "AUD"
## [28] "SSp" "LD" "CP" "EPd" "PIR" "ILA" "TT" "PO" "ORBm"
## [37] "MB" "SCm" "SCsg" "POL" "GPe" "VISrl" "MOp" "LSc" "PT"
## [46] "MD" "LH" "ZI" "SCs" "RN" "MS" "RSP" "PAG" "BLA"
## [55] "VPM" "SSs" "RT" "MEA" "ACB" "OT" "SI" "SNr"Parse Into Dataframe
# Dataframe for the average spks per trial for each session
# Function to calculate the average spks per brain area for the specific trial of a session
firing_rate_brainarea <- function(trialNum, currSession) {
spkTrial = currSession$spks[[trialNum]]
currBrainArea = currSession$brain_area
spkCount = apply(spkTrial, 1, sum)
spkAvg = tapply(spkCount, currBrainArea, mean)
return(spkAvg)
}
# Function to find average spks per brain area for all trials in a session
trial_firing_rate_brainarea <- function(sessionNum) {
trialLen = length(session[[sessionNum]]$feedback_type)
brainareaLen = length(unique(session[[sessionNum]]$brain_area))
trialBrainareaSummary = matrix(nrow = trialLen, ncol = brainareaLen + 2)
for(q in 1:trialLen) {
trialBrainareaSummary[q,] = c(firing_rate_brainarea(q, currSession = session[[sessionNum]]), sessionNum, q)
}
colnames(trialBrainareaSummary) = c(names(firing_rate_brainarea(q,currSession = session[[sessionNum]])), 'session_number', 'trial_number')
trialBrainareaSummary <- as_tibble(trialBrainareaSummary)
# Plot the spikes per area in each session against trials
colorPalette = brewer.pal(n=brainareaLen, name = 'Paired')
plot(0~1, col='white',xlim=c(0,trialLen),ylim=c(0.5,2.2), xlab="Trials",ylab="Average Spike Counts", main=paste("Spikes per Area in Session", sessionNum))
for(i in 1:brainareaLen){
lines(y=trialBrainareaSummary[[i]],x=trialBrainareaSummary$trial_number,col=colorPalette[i],lty=2,lwd=1)
lines(smooth.spline(trialBrainareaSummary$trial_number, trialBrainareaSummary[[i]]),col=colorPalette[i],lwd=3)
}
legend("topright",
legend = colnames(trialBrainareaSummary)[1:brainareaLen],
col = colorPalette,
lty = 1,
cex = 0.8
)
# Plot the correlation matrix for the brain areas, see if there are relations between brain areas
correlationMatrixPlot <- suppressWarnings(chart.Correlation(trialBrainareaSummary[, 3:brainareaLen], histogram = TRUE, pch = 7, alpha = 0.3))
correlationMatrixPlot
# Return the dataframe of brain areas for one session
return(trialBrainareaSummary)
}
# Use above function to apply to all sessions, bind them together to make one dataframe of all session brainarea spk averages.
brainarea_firing_rateDF <- data.frame()
for (p in 1:18) {
brainarea_firing_rateDF = bind_rows(brainarea_firing_rateDF, trial_firing_rate_brainarea(p))
}
## Warning in brewer.pal(n = brainareaLen, name = "Paired"): n too large, allowed maximum for palette Paired is 12
## Returning the palette you asked for with that many colors
## Warning in brewer.pal(n = brainareaLen, name = "Paired"): n too large, allowed maximum for palette Paired is 12
## Returning the palette you asked for with that many colors
## Warning in brewer.pal(n = brainareaLen, name = "Paired"): n too large, allowed maximum for palette Paired is 12
## Returning the palette you asked for with that many colors
head(brainarea_firing_rateDF)## ACA CA3 DG LS MOs root SUB VISp
## 1 1.2660550 1.529412 2.323529 1.9352518 0.9380531 0.5000000 2.080000 1.685393
## 2 0.5871560 1.705882 1.558824 1.3669065 0.7787611 0.6666667 1.946667 1.651685
## 3 1.1926606 1.985294 3.411765 1.7266187 0.9203540 1.4444444 2.640000 2.275281
## 4 0.5137615 2.191176 2.205882 1.0575540 0.7079646 1.0555556 2.800000 1.561798
## 5 0.7706422 1.808824 2.058824 1.3021583 0.7876106 0.7777778 2.613333 1.623596
## 6 0.4587156 1.852941 1.058824 0.9064748 0.7345133 0.6111111 1.906667 1.280899
## session_number trial_number CA1 POST VISl VISpm LP MG MRN NB SPF VISam LGd
## 1 1 1 NA NA NA NA NA NA NA NA NA NA NA
## 2 1 2 NA NA NA NA NA NA NA NA NA NA NA
## 3 1 3 NA NA NA NA NA NA NA NA NA NA NA
## 4 1 4 NA NA NA NA NA NA NA NA NA NA NA
## 5 1 5 NA NA NA NA NA NA NA NA NA NA NA
## 6 1 6 NA NA NA NA NA NA NA NA NA NA NA
## LSr TH VISa VPL OLF ORB PL AUD SSp CP EPd LD PIR ILA PO TT ORBm GPe MB POL
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## SCm SCsg VISrl LSc MOp PT LH MD MS RN SCs ZI PAG RSP BLA VPM SSs MEA RT ACB
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## OT SI SNr
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA# In the correlation matrices:
#The distribution of each variable is shown on the diagonal.
#On the bottom of the diagonal : the bivariate scatter plots with a fitted line are displayed
#On the top of the diagonal : the value of the correlation plus the significance level as stars
#Each significance level is associated to a symbol : p-values(0, 0.001, 0.01, 0.05, 0.1, 1) <=> symbols(“***”, “**”, “*”, “.”, " “)# Dataframe with general statistics across sessions
sessionDF <- data.frame()
for (i in 1:18) {
#Create a custom dataframe here with 4 columns for 4 variables
trialDF <- data.frame(matrix(ncol = 5, nrow = 0))
for (j in 1:length(session[[i]]$spks)) {
# Calculate the total number of neurons active
neuronCountTable = table(unlist(session[[i]]$spks[[j]]))
neuronCountValues = as.numeric(neuronCountTable)
numActiveNeurons = length(session[[i]]$spks[[j]]) - neuronCountValues[1] #neuronCountValues[1] is the # of 0's
# Calculate the total number of spks
numSpks = 0
for(k in 1:length(neuronCountTable)) {
numSpks = numSpks + ((k-1) * neuronCountValues[k])
}
# Calculate the proportion of active neurons
activeNeuronProp = numActiveNeurons / length(session[[i]]$spks[[i]])
# Calculate the firing rate (total number of spks != 0 divided by total)
firing_rate = numSpks / (numSpks + neuronCountValues[1])
# Calculate the absolute difference between left and right contrast
contrastDifference = abs(session[[i]]$contrast_left[j] - session[[i]]$contrast_right[j])
# Append to the variables to the trial data frame
trialDF[nrow(trialDF) + 1,] = c(contrastDifference, numActiveNeurons, numSpks, activeNeuronProp, firing_rate)
}
# Append data together using cbind
tempSession = cbind(rep(i,length(session[[i]]$contrast_left)),
seq(1, length(session[[i]]$spks)),
session[[i]]$mouse_name,
session[[i]]$contrast_left,
session[[i]]$contrast_right,
length(session[[i]]$brain_area),
length(unique(session[[i]]$brain_area)),
length(session[[i]]$spks),
trialDF,
session[[i]]$feedback_type
)
# Append each session using rbind
if (i == 1){
sessionDF <- tempSession
}
else {
sessionDF = rbind(sessionDF, tempSession)
}
}
colnames(sessionDF) = c("session_number", "trial_number", "mouse_name", "contrast_left", "contrast_right", "total_number_of_neurons", "number_of_unique_brain_area", "number_of_trials", "contrast_difference", "number_of_active_neurons", "total_number_of_spks", "proportion_of_active_neurons", "firing_rate", "feedback_type")
sessionDF = as.data.frame(sessionDF)
sessionDF$session_number = as.factor(sessionDF$session_number)
#sessionDF$trial_number = as.factor(sessionDF$trial_number)
#sessionDF$contrast_left = as.factor(sessionDF$contrast_left)
#sessionDF$contrast_right = as.factor(sessionDF$contrast_right)
#sessionDF$feedback_type = as.factor(sessionDF$feedback_type)
head(sessionDF)## session_number trial_number mouse_name contrast_left contrast_right
## 1 1 1 Cori 0.0 0.5
## 2 1 2 Cori 0.0 0.0
## 3 1 3 Cori 0.5 1.0
## 4 1 4 Cori 0.0 0.0
## 5 1 5 Cori 0.0 0.0
## 6 1 6 Cori 0.0 0.0
## total_number_of_neurons number_of_unique_brain_area number_of_trials
## 1 734 8 114
## 2 734 8 114
## 3 734 8 114
## 4 734 8 114
## 5 734 8 114
## 6 734 8 114
## contrast_difference number_of_active_neurons total_number_of_spks
## 1 0.5 1099 1161
## 2 0.0 924 963
## 3 0.5 1281 1354
## 4 0.0 952 1014
## 5 0.0 966 1046
## 6 0.0 747 803
## proportion_of_active_neurons firing_rate feedback_type
## 1 0.03743188 0.03946027 1
## 2 0.03147139 0.03275622 1
## 3 0.04363079 0.04600279 -1
## 4 0.03242507 0.03446401 -1
## 5 0.03290191 0.03552989 -1
## 6 0.02544278 0.02729807 1Plots
# Box plots of firing rate (or numberofactiveneurons, total num of spikes, or proportion) on feedbacktype
# Mention how spks and active neurons are different, since there can be multiple spks per neuron
# Box Plots
# Firing Rate on Feedback Type
firingRate_by_feedback_boxPlot <- ggplot(sessionDF, aes(x = as.factor(feedback_type), y = firing_rate)) + geom_boxplot(outlier.color="red", outlier.shape=8, outlier.size=2, alpha = 0.2) + xlab("Feedback Type") + ylab("Firing Rate") + ggtitle("Box Plot of Firing Rate on Feedback Type") + theme(legend.position="none")
# Number of Active Neurons on Feedback Type
numActiveNeurons_by_feedback_boxPlot <- ggplot(sessionDF, aes(x = as.factor(feedback_type), y = number_of_active_neurons)) + geom_boxplot(outlier.color="red", outlier.shape=8, outlier.size=2, alpha = 0.2) + xlab("Feedback Type") + ylab("Number of Active Neurons") + ggtitle("Box Plot of Number of Active Neurons on Feedback Type") + theme(legend.position="none")
# Total Number of Spikes on Feedback Type
totalNumSpikes_by_feedback_boxPlot <- ggplot(sessionDF, aes(x = as.factor(feedback_type), y = total_number_of_spks)) + geom_boxplot(outlier.color="red", outlier.shape=8, outlier.size=2, alpha = 0.2) + xlab("Feedback Type") + ylab("Total Number of Spikes") + ggtitle("Box Plot of Total Number of Spikes on Feedback Type") + theme(legend.position="none")
# Proportion of Active Neurons on Feedback Type
proportionActiveNeurons_by_feedback_boxPlot <- ggplot(sessionDF, aes(x = as.factor(feedback_type), y = proportion_of_active_neurons)) + geom_boxplot(outlier.color="red", outlier.shape=8, outlier.size=2, alpha = 0.2) + xlab("Feedback Type") + ylab("Proportion of Active Neurons") + ggtitle("Box Plot of Proportion of Active Neurons on Feedback Type") + theme(legend.position="none")
grid.arrange(firingRate_by_feedback_boxPlot, proportionActiveNeurons_by_feedback_boxPlot, numActiveNeurons_by_feedback_boxPlot, totalNumSpikes_by_feedback_boxPlot, nrow = 2, ncol = 2)
# Correlation Matrices for each Session
for (i in 1:18) {
corrplot.mixed(round(cor(sessionDF[sessionDF$session_number == i, c('contrast_left', 'contrast_right', 'contrast_difference', 'number_of_active_neurons', 'total_number_of_spks', 'proportion_of_active_neurons', 'firing_rate', 'feedback_type')]), 2), title = paste("Correlation Matrix of Session", i), mar=c(1,1,1,1))
}
# Correlation Matrices for Each Mouse
mouseNameVector <- c('Cori', 'Forssmann', 'Hench', 'Lederberg')
for (i in 1:length(mouseNameVector)) {
corrplot.mixed(round(cor(sessionDF[sessionDF$mouse_name == mouseNameVector[i], c('contrast_left', 'contrast_right', 'number_of_active_neurons', 'total_number_of_spks', 'proportion_of_active_neurons', 'firing_rate', 'feedback_type')]), 2), title = paste("Correlation Matrix of Mouse", mouseNameVector[i]), mar=c(1,1,1,1))
}
# Plots for variables against trials (for individual session analysis)
for (i in 1:18) {
plot1 <- ggplot(sessionDF[sessionDF$session_number == i,], aes(x = trial_number, y = number_of_active_neurons)) + geom_point() + geom_smooth(formula = y ~ x, method="lm") + ggtitle("Number of Active Neurons Against Trials") + labs(y = "Number of Active Neurons", x = "Trials")
plot2 <- ggplot(sessionDF[sessionDF$session_number == i,], aes(x = trial_number, y = total_number_of_spks)) + geom_point() + geom_smooth(formula = y ~ x, method="lm") + ggtitle("Total Number of Spikes Against Trials") + labs(y = "Total Number of Spikes", x = "Trials")
plot3 <- ggplot(sessionDF[sessionDF$session_number == i,], aes(x = trial_number, y = proportion_of_active_neurons)) + geom_point() + geom_smooth(formula = y ~ x, method="lm") + ggtitle("Proportion of Active Neurons Against Trials") + labs(y = "Proportion of Active Neurons", x = "Trials")
plot4 <- ggplot(sessionDF[sessionDF$session_number == i,], aes(x = trial_number, y = firing_rate)) + geom_point() + geom_smooth(formula = y ~ x, method="lm") + ggtitle("Firing Rate Against Trials") + labs(y = "Firing Rate", x = "Trials")
grid.arrange(plot1, plot2, plot3, plot4, nrow = 2, ncol = 2, top = textGrob(paste("Session", i, "Scatterplots")))
}
# Plots for variables against trials (for session analysis)
ggplot(sessionDF, aes(x = trial_number, y = number_of_active_neurons, colour = session_number)) + geom_point() + geom_smooth(method = "loess", formula = y ~ x) + ggtitle("Scatterplot of Number of Active Neurons Against Trials") + labs(y = "Number of Active Neurons", x = "Trials")
ggplot(sessionDF, aes(x = trial_number, y = total_number_of_spks, colour = session_number)) + geom_point() + geom_smooth(method = "loess", formula = y ~ x) + ggtitle("Scatterplot of Total Number of Spikes Against Trials") + labs(y = "Total Number of Spikes", x = "Trials")
ggplot(sessionDF, aes(x = trial_number, y = proportion_of_active_neurons, colour = session_number)) + geom_point() + geom_smooth(method = "loess", formula = y ~ x) + ggtitle("Scatterplot of Proportion of Active Neurons Against Trials") + labs(y = "Proportion of Active Neurons", x = "Trials")
ggplot(sessionDF, aes(x = trial_number, y = firing_rate, colour = session_number)) + geom_point() + geom_smooth(method = "loess", formula = y ~ x) + ggtitle("Scatterplot of Firing Rate Against Trials") + labs(y = "Firing Rate", x = "Trials")
# Combine both dataframes to one large one
completeDF <- cbind(sessionDF, subset(brainarea_firing_rateDF, select = -c(session_number, trial_number) ))
head(completeDF)## session_number trial_number mouse_name contrast_left contrast_right
## 1 1 1 Cori 0.0 0.5
## 2 1 2 Cori 0.0 0.0
## 3 1 3 Cori 0.5 1.0
## 4 1 4 Cori 0.0 0.0
## 5 1 5 Cori 0.0 0.0
## 6 1 6 Cori 0.0 0.0
## total_number_of_neurons number_of_unique_brain_area number_of_trials
## 1 734 8 114
## 2 734 8 114
## 3 734 8 114
## 4 734 8 114
## 5 734 8 114
## 6 734 8 114
## contrast_difference number_of_active_neurons total_number_of_spks
## 1 0.5 1099 1161
## 2 0.0 924 963
## 3 0.5 1281 1354
## 4 0.0 952 1014
## 5 0.0 966 1046
## 6 0.0 747 803
## proportion_of_active_neurons firing_rate feedback_type ACA CA3
## 1 0.03743188 0.03946027 1 1.2660550 1.529412
## 2 0.03147139 0.03275622 1 0.5871560 1.705882
## 3 0.04363079 0.04600279 -1 1.1926606 1.985294
## 4 0.03242507 0.03446401 -1 0.5137615 2.191176
## 5 0.03290191 0.03552989 -1 0.7706422 1.808824
## 6 0.02544278 0.02729807 1 0.4587156 1.852941
## DG LS MOs root SUB VISp CA1 POST VISl VISpm
## 1 2.323529 1.9352518 0.9380531 0.5000000 2.080000 1.685393 NA NA NA NA
## 2 1.558824 1.3669065 0.7787611 0.6666667 1.946667 1.651685 NA NA NA NA
## 3 3.411765 1.7266187 0.9203540 1.4444444 2.640000 2.275281 NA NA NA NA
## 4 2.205882 1.0575540 0.7079646 1.0555556 2.800000 1.561798 NA NA NA NA
## 5 2.058824 1.3021583 0.7876106 0.7777778 2.613333 1.623596 NA NA NA NA
## 6 1.058824 0.9064748 0.7345133 0.6111111 1.906667 1.280899 NA NA NA NA
## LP MG MRN NB SPF VISam LGd LSr TH VISa VPL OLF ORB PL AUD SSp CP EPd LD PIR
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## ILA PO TT ORBm GPe MB POL SCm SCsg VISrl LSc MOp PT LH MD MS RN SCs ZI PAG
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## RSP BLA VPM SSs MEA RT ACB OT SI SNr
## 1 NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA# Scatterplot for Mice Brain areas on feedback type per mouse
for (i in 1:18) {
tempBrainAreaDF <- completeDF[completeDF$session_number == i, colSums(is.na(completeDF[completeDF$session_number == i,])) == 0]
tempBrainAreaDF <- melt(tempBrainAreaDF, id.vars = "feedback_type", measure.vars = colnames(tempBrainAreaDF)[14:length(colnames(tempBrainAreaDF))])
tempPlot <- ggplot(tempBrainAreaDF) + geom_boxplot(aes(x = as.factor(feedback_type), y = value, color = variable), outlier.color="red", outlier.shape=8, outlier.size=2, alpha = 0.2) + xlab("Feedback Type") + ylab("Firing Rate") + ggtitle(paste("Session", i, "Box Plot of Brain Area Firing Rate on Feedback Type")) + guides(color = guide_legend(title = "Brain Areas"))
print(tempPlot)
}
# Scatterplot of mice brainarea firing rate by trial per mouse
tempBrainAreaPerMouse <- completeDF[completeDF$mouse_name == mouseNameVector[4], ]
tempBrainAreaPerMouse <- tempBrainAreaPerMouse[,colSums(is.na(tempBrainAreaPerMouse))<nrow(tempBrainAreaPerMouse)]
tempBrainAreaPerMouse <- tempBrainAreaPerMouse[, 14:length(colnames(tempBrainAreaPerMouse))]
tempBrainAreaPerMouse[is.na(tempBrainAreaPerMouse)] <- 0
ggplot(melt(tempBrainAreaPerMouse), aes(x = variable, y = value)) + geom_boxplot(outlier.color="red", outlier.shape=8, outlier.size=2, alpha = 0.2) + xlab("Brain Areas") + ylab("Firing Rate") + ggtitle(paste("Mouse", mouseNameVector[4], "Box Plots of Brain Area Firing Rates"))## No id variables; using all as measure variables
# Create general plot so that we can use clustering to find the specific sessions that we can combine into one dataframe
# Do this for different variables (eg. proportion of active neurons, etc)
# This new dataframe will hold the values that provide information for a better predictie model
# For part 2, combine specific sessions that give us information
# For example, if session 2 and 19 shows both similar sharp slopes, combine these two dataframes
# Can do kmeans on multiple dimensions aka multiple variables, do 2-7 clusters
clusterDF <- sessionDF[, 4:14]
clusterDF <- clusterDF[, !names(clusterDF) %in% c("number_of_trials")]
clusterDF <- cbind(seq(1, nrow(clusterDF), by=1), clusterDF)
colnames(clusterDF)[1] = "id"
# Clustering data for 3 clusters
clusterDF.k3 <- clusterDF %>% kmeans(3)
clusterIndicatorVector <- clusterDF.k3$cluster
# Append the cluster indicator to the dataframes for further analysis
completeDF <- cbind(clusterIndicatorVector, completeDF)
clusterDF <- cbind(clusterIndicatorVector, clusterDF)
colnames(completeDF)[1] <- "cluster_id"
colnames(clusterDF)[1] <- "cluster_id"# Plots demonstrating clusters
clusterDF %>% mutate(cluster = clusterDF.k3$cluster) %>%
ggplot(aes(x = number_of_active_neurons, y = firing_rate, color = as.factor(cluster))) +
geom_point()
clusterDF %>% mutate(cluster = clusterDF.k3$cluster) %>%
ggplot(aes(x = number_of_unique_brain_area, y = total_number_of_spks, color = as.factor(cluster))) +
geom_point()
clusterDF %>% mutate(cluster = clusterDF.k3$cluster) %>%
ggplot(aes(x = total_number_of_spks, y = firing_rate, color = as.factor(cluster))) +
geom_point()
# Which contrast has most effect on firing rate
clusterDF %>% mutate(cluster = clusterDF.k3$cluster) %>%
ggplot(aes(x = contrast_left, y = firing_rate, color = as.factor(cluster))) +
geom_point()
clusterDF %>% mutate(cluster = clusterDF.k3$cluster) %>%
ggplot(aes(x = contrast_right, y = firing_rate, color = as.factor(cluster))) +
geom_point()
# Separate the complete dataframe into 3 separate dataframes based on cluster_id
cluster1DF <- completeDF[completeDF$cluster_id == 1,]
cluster2DF <- completeDF[completeDF$cluster_id == 2,]
cluster3DF <- completeDF[completeDF$cluster_id == 3,]
# Correlation matrices for new dataframe clusters
ggcorrplot(round(cor(cluster1DF[, 5:15]), 1), hc.order = TRUE,
type = "lower", lab =TRUE, p.mat = cor_pmat(cluster1DF[, 5:15]))
ggcorrplot(round(cor(cluster2DF[, 5:15]), 1), hc.order = TRUE,
type = "lower", lab =TRUE, p.mat = cor_pmat(cluster2DF[, 5:15]))
ggcorrplot(round(cor(cluster3DF[, 5:15]), 1), hc.order = TRUE,
type = "lower", lab =TRUE, p.mat = cor_pmat(cluster3DF[, 5:15]))
# TODO:
# for part 2, figure out which variables to include in the model for part 3.
# use DT::datatable() to show all dataframe variables, do this for original dataframe and dataframe that we chose for predictive model (this one will have less variables and rows)
# I will use variables: firing_rate, number_of_active_neurons, total_number_of_neurons, number_of_unique_brain_area, contrast_difference. (proportion of active neurons is too similiar to firing rate, and total_number_of_spks is too similar to number of active neurons. )Extra
dat <- session[[1]]
#session[[1]]$brain_area
unique(dat$brain_area)## [1] "ACA" "MOs" "LS" "root" "VISp" "CA3" "SUB" "DG"i_trial = 12;
i_session = 1;
dim(session[[i_session]]$spks[[i_trial]])## [1] 734 40length(session[[i_session]]$feedback_type)## [1] 114apply(session[[i_session]]$spks[[i_trial]], 2, sum)## [1] 24 30 27 22 24 30 34 36 40 35 41 27 37 27 36 53 38 49 36 45 45 40 39 27 36
## [26] 44 44 46 48 53 32 38 29 23 30 27 19 37 18 32names(session[[2]])## [1] "contrast_left" "contrast_right" "feedback_type" "mouse_name"
## [5] "brain_area" "date_exp" "spks" "time"#Use different spks trials to make predictive models (logistic regression will work, show some linear or svm)
#session[[i_session]]$spks[[i_trial]]
#to predict:
#session[[i_session]]$feedback_type[i_trial]
# LOOK AT COURSE PROJECT DESCRIPTION .RMD FOR VARIABLE EXPLORATION
#Ask if we are using the trials of spks in combination with other recorded variables (eg. brain_area, contrast_left, contrast_right) to predict the feedback_type. Yes
#Predict the feedback_type per trial, not session
# Create df including all sessions, each session df will have rows as trials, and the columns will be variables of each trial (contrast_left, contrast_right, firing_rate, feedback_type). Use bind to stack each session df.session[[5]]$spks[[11]]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
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## [1077,] 0 0 0session[[5]]$contrast_left[11]## [1] 1session[[5]]$contrast_right[11]## [1] 0session[[5]]$feedback_type[11]## [1] 1length(session[[5]]$spks[[1]])## [1] 43080table(unlist(session[[5]]$spks[1]))##
## 0 1 2 3
## 41785 1189 97 9length(session[[5]]$spks)## [1] 254#Why is this different
#length(session[[1]]$spks[[1]])
#length(session[[1]]$spks[1])
#if this results in same
#session[[1]]$spks[[1]]
#session[[1]]$spks[1]